Analysis and Performance Evaluation of the ZEM/ZEV Guidance and its Sliding Robustification for Autonomous Rendezvous in Relative Motion

Devising closed-loop guidance algorithms for autonomous relative motion is an important problem within the field of orbital dynamics. In this paper, we study the guided relative motion of two spacecraft for which one of them is executing an autonomous rendezvous via the ZEM/ZEV feedback guidance and its robustified Optimal Sliding Guidance (OSG) counterpart. Starting from the classical Clohessy-Wiltshire (CW) model, we systematically analyze the ability of the ZEM/ZEV feedback guidance to generate closed loop trajectories that drive the deputy spacecraft to the chief satellite and evaluate its performance in terms of target accuracy and propellant consumption. It is shown that the guidance gains and the time of flight predicted by the theoretical solution generates a class of feedback trajectories that are accurate but suboptimal with respect to the open-loop fuel-optimal solution. Indeed, a parametric study shows that a different set of gains may generate relative guided trajectories that yields fuel consumption closer to the ideal optimal. The guidance algorithms are also demonstrated to be accurate in guiding the relative motion of the deputy toward a chief spacecraft in highly elliptical orbit where the Linearized Equations of Relative Motions (LERM) are employed to compute the Zero-Effort-Miss (ZEM) and Zero-Effort-Velocity (ZEV) necessary to compute the acceleration command as prescribed by the theory

Machine Learning Methods for Neonatal Mortality and Morbidity Classification

Preterm birth is the leading cause of mortality in children under the age of five. In particular, low birth weight and low gestational age are associated with an increased risk of mortality. Preterm birth also increases the risks of several complications, which can increase the risk of death, or cause long-term morbidities with both individual and societal impacts. In this work, we use machine learning for prediction of neonatal mortality as well as neonatal morbidities of bronchopulmonary dysplasia, necrotizing enterocolitis, and retinopathy of prematurity, among very low birth weight infants. Our predictors include time series data and clinical variables collected at the neonatal intensive care unit of Children's Hospital, Helsinki University Hospital. We examine 9 different classifiers and present our main results in AUROC, similar to our previous studies, and in F1-score, which we propose for classifier selection in this study. We also investigate how the predictive performance of the classifiers evolves as the length of time series is increased, and examine the relative importance of different features using the random forest classifier, which we found to generally perform the best in all tasks. Our systematic study also involves different data preprocessing methods which can be used to improve classifier sensitivities. Our best classifier AUROC is 0.922 in the prediction of mortality, 0.899 in the prediction of bronchopulmonary dysplasia, 0.806 in the prediction of necrotizing enterocolitis, and 0.846 in the prediction of retinopathy of prematurity. Our best classifier F1-score is 0.493 in the prediction of mortality, 0.704 in the prediction of bronchopulmonary dysplasia, 0.215 in the prediction of necrotizing enterocolitis, and 0.368 in the prediction of retinopathy of prematurity.Peer reviewe

Uncertainty-aware deep learning methods for robust diabetic retinopathy classification

Automatic classification of diabetic retinopathy from retinal images has been increasingly studied using deep neural networks with impressive results. However, there is clinical need for estimating uncertainty in the classifications, a shortcoming of modern neural networks. Recently, approximate Bayesian neural networks (BNNs) have been proposed for this task, but previous studies have only considered the binary referable/non-referable diabetic retinopathy classification applied to benchmark datasets. We present novel results for 9 BNNs by systematically investigating a clinical dataset and 5-class classification scheme, together with benchmark datasets and binary classification scheme. Moreover, we derive a connection between entropy-based uncertainty measure and classifier risk, from which we develop a novel uncertainty measure. We observe that the previously proposed entropy-based uncertainty measure improves performance on the clinical dataset for the binary classification scheme, but not to such an extent as on the benchmark datasets. It improves performance in the clinical 5-class classification scheme for the benchmark datasets, but not for the clinical dataset. Our novel uncertainty measure generalizes to the clinical dataset and to one benchmark dataset. Our findings suggest that BNNs can be utilized for uncertainty estimation in classifying diabetic retinopathy on clinical data, though proper uncertainty measures are needed to optimize the desired performance measure. In addition, methods developed for benchmark datasets might not generalize to clinical datasets

Diabetes is associated with familial idiopathic normal pressure hydrocephalus : a case-control comparison with family members

Background The pathophysiological basis of idiopathic normal pressure hydrocephalus (iNPH) is still unclear. Previous studies have shown a familial aggregation and a potential heritability when it comes to iNPH. Our aim was to conduct a novel case-controlled comparison between familial iNPH (fNPH) patients and their elderly relatives, involving multiple different families. Methods Questionnaires and phone interviews were used for collecting the data and categorising the iNPH patients into the familial (fNPH) and the sporadic groups. Identical questionnaires were sent to the relatives of the potential fNPH patients. Venous blood samples were collected for genetic studies. The disease histories of the probable fNPH patients (n = 60) were compared with their >= 60-year-old relatives with no iNPH (n = 49). A modified Charlson Comorbidity Index (CCI) was used to measure the overall disease burden. Fisher's exact test (two-tailed), the Mann-Whitney U test (two-tailed) and a multivariate binary logistic regression analysis were used to perform the statistical analyses. Results Diabetes (32% vs. 14%, p = 0.043), arterial hypertension (65.0% vs. 43%, p = 0.033), cardiac insufficiency (16% vs. 2%, p = 0.020) and depressive symptoms (32% vs. 8%, p = 0.004) were overrepresented among the probable fNPH patients compared to their non-iNPH relatives. In the age-adjusted multivariate logistic regression analysis, diabetes remained independently associated with fNPH (OR = 3.8, 95% CI 1.1-12.9, p = 0.030). Conclusions Diabetes is associated with fNPH and a possible risk factor for fNPH. Diabetes could contribute to the pathogenesis of iNPH/fNPH, which motivates to further prospective and gene-environmental studies to decipher the disease modelling of iNPH/fNPH.Peer reviewe

Familial idiopathic normal pressure hydrocephalus

Idiopathic normal pressure hydrocephalus (iNPH) is a late-onset surgically alleviated, progressive disease. We characterize a potential familial subgroup of iNPH in a nation-wide Finnish cohort of 375 shunt-operated iNPH-patients. The patients were questionnaired and phone-interviewed, whether they have relatives with either diagnosed iNPH or disease-related symptomatology. Then pedigrees of all families with more than one iNPH-case were drawn. Eighteen patients (4.8%) from 12 separate pedigrees had at least one shunt-operated relative whereas 42 patients (11%) had relatives with two or more triad symptoms. According to multivariate logistic regression analysis, familial iNPH-patients had up to 3-fold risk of clinical dementia compared to sporadic iNPH patients. This risk was independent from diagnosed Alzheimer's disease and APOE epsilon 4 genotype. This study describes a familial entity of iNPH offering a novel approach to discover the potential genetic characteristics of iNPH. Discovered pedigrees offer an intriguing opportunity to conduct longitudinal studies targeting potential preclinical signs of iNPH. (C) 2016 Elsevier B.V. All rights reserved.Peer reviewe

Diabetes is associated with familial idiopathic normal pressure hydrocephalus: a case-control comparison with family members

Background The pathophysiological basis of idiopathic normal pressure hydrocephalus (iNPH) is still unclear. Previous studies have shown a familial aggregation and a potential heritability when it comes to iNPH. Our aim was to conduct a novel case-controlled comparison between familial iNPH (fNPH) patients and their elderly relatives, involving multiple different families. Methods Questionnaires and phone interviews were used for collecting the data and categorising the iNPH patients into the familial (fNPH) and the sporadic groups. Identical questionnaires were sent to the relatives of the potential fNPH patients. Venous blood samples were collected for genetic studies. The disease histories of the probable fNPH patients (n = 60) were compared with their >= 60-year-old relatives with no iNPH (n = 49). A modified Charlson Comorbidity Index (CCI) was used to measure the overall disease burden. Fisher's exact test (two-tailed), the Mann-Whitney U test (two-tailed) and a multivariate binary logistic regression analysis were used to perform the statistical analyses. Results Diabetes (32% vs. 14%, p = 0.043), arterial hypertension (65.0% vs. 43%, p = 0.033), cardiac insufficiency (16% vs. 2%, p = 0.020) and depressive symptoms (32% vs. 8%, p = 0.004) were overrepresented among the probable fNPH patients compared to their non-iNPH relatives. In the age-adjusted multivariate logistic regression analysis, diabetes remained independently associated with fNPH (OR = 3.8, 95% CI 1.1-12.9, p = 0.030). Conclusions Diabetes is associated with fNPH and a possible risk factor for fNPH. Diabetes could contribute to the pathogenesis of iNPH/fNPH, which motivates to further prospective and gene-environmental studies to decipher the disease modelling of iNPH/fNPH.</div

Performance evaluation of artificial neural network-based shaping algorithm for planetary pinpoint guidance

Computational intelligence techniques have been used in a wide range of application areas. This paper proposes a new learning algorithm that dynamically shapes the landing trajectories, based on potential function methods, in order to provide computationally efficient on-board guidance and control. Extreme Learning Machine (ELM) devises a Single Layer Forward Network (SLFN) to learn the relationship between the current spacecraft position and the optimal velocity field. The SLFN design is tested and validated on a set of data comprising data points belonging to the training set on which the network has not been trained. Furthermore, the proposed efficient algorithm is tested in typical simulation scenarios which include a set of Monte Carlo simulation to evaluate the guidance performance

Performance Evaluation of Artificial Neural Network-Based Shaping Algorithm for Planetary Pinpoint Guidance

Computational intelligence techniques have been used in a wide range of application areas. This paper proposes a new learning algorithm that dynamically shapes the landing trajectories, based on potential function methods, in order to provide computationally efficient on-board guidance and control. Extreme Learning Machine (ELM) devises a Single Layer Forward Network (SLFN) to learn the relationship between the current spacecraft position and the optimal velocity field. The SLFN design is tested and validated on a set of data comprising data points belonging to the training set on which the network has not been trained. Furthermore, the proposed efficient algorithm is tested in typical simulation scenarios which include a set of Monte Carlo simulation to evaluate the guidance performances

Optimal Sliding Guidance for Earth-Moon Halo Orbit Station-Keeping and Transfer

An Optimal Sliding Guidance (OSG) is implemented in the Circular Restricted Three-Body Problem and Restricted Four-Body Problem for spacecraft near libration points of the Earth-Moon system. Based on a combination of generalized Zero-Effort-Miss/Zero-Effort-Velocity and time-dependent sliding control theory, OSG is capable of generating closed-loop guided trajectories that are demonstrated to be globally finite-time stable against uncertain perturbing accelerations with known upper bound. The application of the OSG for Halo orbit station-keeping and orbital transfer are studied in a perturbed four body dynamical model in order to evaluate response and effectiveness of the proposed guidance approach